SmartenIT: Socially-aware Management of New Overlay Application Traffic combined with Energy Efficiency in the Internet

Summary

The Internet has seen a strong move to support overlay applications, which demand a coherent and integrated control in underlying heterogeneous networks in a scalable, resilient, and energy-efficient manner. A tighter integration of network management and overlay service functionality can lead to cross-layer optimization of operations and management, thus, being a promising approach to offer a large business potential in operational perspectives for all players involved. Therefore, SmartenIT (Socially-aware Management of New Overlay Application Traffic combined with Energy Efficiency in the Internet) targets at an incentive-compatible cross-layer network management for providers of overlay-based application (e.g., cloud applications, content delivery, and social networks), network providers, and end-users to ensure a QoE-awareness, by addressing accordingly load and traffic patterns or special application requirements, and exploiting at the same time social awareness (in terms of user relations and interests). Moreover, energy efficiency with respect to both end-user devices and underlying networking infrastructure is tackled to ensure an operationally efficient management. Incentive-compatible network management mechanisms for improving metrics on an inter-domain basis for ISPs serve as the major mechanism to deal with and investigate real-life scenarios.

HTTP Adaptive Streaming (HAS) technologies, e.g., Apple HLS or MPEG-DASH, automatically adapt the delivered video quality to the available network. This reduces stalling of the video but additionally introduces quality switches, which also influence the user-perceived Quality of Experience (QoE). In this work, we conduct a subjective study to identify the impact of adaptation parameters on QoE. The results indicate that the video quality has to be maximized first, and that the number of quality switches is less important. Based on these results, a method to compute the optimal QoE-optimal adaptation strategy for HAS on a per user basis with mixed-integer linear programming is presented. This QoE-optimal adaptation enables the benchmarking of existing adaptation algorithms for any given network condition. Moreover, the investigated concept is extended to a multi-user IPTV scenario. The question is answered whether video quality, and thereby, the QoE can be shared in a fair manner among the involved users.

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services which relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows to take into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this article as fundament to derive the QoE influence factors which emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies which cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, the article is a major step towards truly improving HAS.

The term Software Defined Networking (SDN) is prevalent in today’s discussion about future communication networks. As with any new term or paradigm, however, no consistent definition regarding this technology has formed. The fragmented view on SDN results in legacy products being passed off by equipment vendors as SDN, academics mixing up the attributes of SDN with those of network virtualization, and users not fully understanding the benefits. Therefore, establishing SDN as a widely adopted technology beyond laboratories and insular deployments requires a compass to navigate the multitude of ideas and concepts that make up SDN today. The contribution of this article represents an important step toward such an instrument. It gives a thorough definition of SDN and its interfaces as well as a list of its key attributes. Furthermore, a mapping of interfaces and attributes to SDN use cases is provided, highlighting the relevance of the interfaces and attributes for each scenario. This compass gives guidance to a potential adopter of SDN on whether SDN is in fact the right technology for a specific use case.

HTTP Adaptive Streaming (HAS) is the de-facto standard for over-the-top (OTT) video streaming services. It allows to react to fluctuating network conditions on short time scales by adapting the video bit rate in order to avoid stalling of the video playback. With HAS the video content is split into small segments of a few seconds playtime each, which are available in different bit rates, i.e., quality level representations. Depending on the current conditions, the adaptation algorithm on the client side chooses the appropriate quality level and downloads the respective segment. This allows to avoid stalling, which is seen as the worst possible disturbance of HTTP video streaming, to the most possible extend. Nevertheless, the user perceived Quality of Experience (QoE) may be affected, namely by playing back lower qualities and by switching between different qualities. Therefore, adaptation algorithms are desired which maximize the user’sQoEfor the currently available network resources. Many downloading strategies have been proposed in literature, but a solid user-centric comparison of these mechanisms among each other and with the global optimum is missing. The major contributions of this work are as follows. A proper analysis of the influence of quality switches and played out representations on QoE is conducted by means of subjective user studies. The results suggest that, in order to optimize QoE, first, the quality level of the video stream has to be maximized and second, the number of quality switches should be minimized. Based on our findings, a QoEoptimization problem is formulated and the performance of our proposed algorithm is compared to other algorithms and to the QoE-optimal adaptation.

Completing their initial phase of rapid growth, social networks are expected to reach a plateau from where on they are in a statistically stationary state. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state with a directed flow of information. To distinguish between the two situations, we propose a quantity called entropy production that was introduced in statistical physics as a measure for non-vanishing probability currents in nonequilibrium stationary states. The proposed quantity closes a gap for characterizing online social networks. As major contribution, we show the relation and difference between entropy production and existing metrics. The comparison shows that computational intensive metrics like centrality can be approximated by entropy production for typical online social networks. To compute the entropy production from real-world measurements, the need for Bayesian inference and the limits of naïve estimates for those probability currents are shown. As further contribution, a general scheme is presented to measure the entropy production in small-world networks using Bayesian inference. The scheme is then applied for a specific example of the R mailing list.